P1900.8 - Standard for Training, Testing, and Evaluating Machine-Learned Spectrum Awareness Models
Project Details
This standard describes how to generate datasets to train and test Machine-Learned Spectrum Awareness (MLSA) models that detect, classify, characterize, and/or identify radio frequency (RF) signals and signal emitters. The scope of this standard includes: methods for creating training and test datasets for MLSA models that are representative of real-world Dynamic Spectrum Access (DSA) and spectrum sharing scenarios, methods for using data augmentation techniques to introduce sufficient sample variation so that the MLSA model can generalize to real-world scenarios, methods for enhancing the training dataset with RF propagation channels and interference sources that are representative of real-world scenarios, specifications for how to structure and store MLSA datasets, methods for creating secure and performant MLSA models that operate on resource-constrained RF sensors and processors, and finally, criteria for evaluating the performance of MLSA models.
Working Group Details
Working Group
Sponsor Committee
IEEE Program Manager